1. Field of the Invention
The present invention generally relates to a computer implemented and/or assisted process for one or more of eliminating redundant, repetitive surveying of patients with multiple medical conditions, identifying potentially high health care resource utilizers in a population, improving clinical information for use by drug utilization review, care management and prior authorization programs, and/or identifying likely candidates for other health management programs while improving or maintaining the quality of care in a patient population. More particularly, the present invention relates to a computer implemented and/or assisted process for creating a central comprehensive information profile on the health of the patient utilizing, among other sources of information, utilizing medical claims and drug claim files and the interactive selection and/or collection of extensive information on a patient's current health condition, demographics, and risk behaviors. The present invention also relates to assisting, optionally in real-time, the identification of potential drug-disease and drug-drug adverse affects. By combining diagnoses information, vital statistic information and drug claim related information from a variety of internal and external sources and databases, the present invention facilitates the delivery of the highest quality care.
2. Background of the Related Art
Health care costs continue to be a significant portion of the United States Gross National Product, and are still rising. Health care cost containment programs include, among other things, drug utilization reviews (“DUR”), prior authorization of medical care, programs which allow patients to obtain diagnostic advice directly concerning specific problems and the identification and use of intervention programs to prevent future health problems or the progression of disease.
A significant portion of these increased expenses represent costs attributed to individuals who utilize health care services to a higher degree than average. It has been determined that the lack of understanding of the nature and likely needs of such potentially high health care users hinders the ability to target interventions appropriately, and thereby address the medical needs of these individual patients more appropriately.
The administrative cost of enrolling high utilization patients in intervention programs has been high, in part, because patients enrolled in programs to manage inappropriate utilization have been required to complete multiple questionnaires for an overall assessment of health. These questionnaires or surveys are burdensome to the patient and costly for the program. A few of the questionnaires may assess health risk appropriately, but these tools are not designed to, nor predict future resource utilization.
Accordingly, we have determined that many patients are deprived of access to, or the benefit of, the most appropriate health care program and information, which is currently unable to be practically obtained. For example, many people delay in obtaining, or are prevented from seeking, medical attention because of cost, time constraints, or inconvenience. If the health management programs had easy access to updated information on the medical condition of the patient and likely health management needs, many diseases could be more effectively and efficiently treated. Similarly, the early detection and treatment of numerous diseases could keep or prevent many patients from reaching the advanced stages of illness. These advanced stages of illness are a significant part of the costs attributed to our nation's health care system.
To date, no prior art which the inventors are aware of that integrates survey and diagnostic data into a predictive model for high medical resource utilization, create a central portfolio of patient data for assessment of the effects of the health management programs, use this information in providing health care management and prior authorization, and/or identification of and enrollment of candidates for other health management programs. As a way of providing background, some of these other programs are described below.
For example, one prior attempt at a solution to the health care problem is called Ask-A-Nurse, wherein a group of nurses provide health information by telephone around-the-clock. A can call an 800 number 24×7 to obtain health advise from a nurse. The nurse uses a computer for general or diagnostic information on the ailment or complaint mentioned by the caller. The nurse may then refer the caller to a doctor from a computerized referral list for a contracting hospital or group of hospitals. Client hospitals contract with Ask-A-Nurse to provide patient referrals. A managed care option called Personal Health Advisor is similar and adds the capability for the caller to hear prerecorded messages on health topics 24 hours a day.
Several problems exist with these types of prior medical advice systems. First, these systems have high costs associated with having a nurse answer each telephone call. Second, the caller may have to belong to a participating health plan to utilize the service. Third, and significantly, this system is designed to respond to reactive problems determined by the caller, and therefore, provides no ability to eliminate the possibility of such a condition occurring in the first instance, or proactively preventing the condition from occurring. Further, these advice systems are not implementable for large patient populations, particularly where the proactive measures are patient-specific.
However, other inventions are available, such as a computerized service that answers health care questions and advises people in their homes. A health maintenance organization (HMO) may provide this service to its members in a particular geographic area. To get advice at home, an HMO member connects a toaster-sized box to a telephone and calls a toll-free number. Using a keyboard that is part of the box, the user answers questions displayed on a screen of the box relating to the user's symptoms. Depending on the answers, the user might be told to try a home remedy, be called by a nurse or doctor, or be given an appointment to be examined. As with Ask-A-Nurse program, this system is also designed to respond to reactive problems determined by the caller, and therefore, provides no ability to eliminate the possibility of such a condition occurring in the first instance.
At the other end of the spectrum, are various attempts at analyzing retroactively using hindsight, the appropriateness of the delivered medical care for quality and cost. For example, U.S. Pat. No. 5,544,044 to Leatherman et al., incorporated herein by reference, relates to a software-based medical information system that analyzes health care claims records for an enrolled population (e.g., HMO, Medicaid) to assess and report on quality of care based on conformance to nationally recognized medical practice guidelines or quality indicators (See FIGS. 1 through 4, from Leatherman et al.).
The process is typically performed at the request of a customer that is a health maintenance organization, indemnity insurer (e.g., Blue Cross), a large, self-insured employer group or a government program (e.g., Medicaid). The process begins by obtaining the customer specific parameters, such as what time period the customer wishes to analyze or whether the customer wants to have some data broken down by particular providers or other grouping variables 1. The next step 2 updates the system options and parameters using the customer specifications. Thereafter, the system obtains and loads the customer data 3, usually consisting of the customer's already-computerized health care claims data for a specified period, together with enrollment data and health care provider data.
The enrollment data are extracted 4 so as to identify the enrollees served by the customer that meet a predefined enrollment criterion. The resulting enrollment data contains one record per enrollee. Next, the relevant claims data are extracted from the complete customer data base 5 and are configured through linkages to produce the necessary health records. The claims data includes claims records for medical professional services (outpatient records) 6, claims records for hospital services (inpatient records) 7 and claims records for pharmacy purchases (pharmacy records) 8.
If the customer desires, provider-specific data is also extracted from the customer data 9, permitting the later analysis to be broken down by the particular provider of services or products, which may be a particular doctor, clinic or hospital. The resulting files are merged to produce uncorrected master files 11. Duplicate claims are excluded, and claims that have been reversed through the claims adjudication process are also excluded. This produces a master file of health care claims records.
This prior art also involves the application of the definitions for the health care condition to identify the population having that condition, followed by an analysis of the claims records for that population (a subset of the master files) under the defined quality care criteria 12 (FIG. 2).
The result of the analysis is a report that includes: charts and graphs reporting statistically observed quality of care data in the population defined as having the health care condition of interest 13, a written analysis reporting from a care quality viewpoint 14, statistical results considered worthy of highlighting and a report containing recommendations for actions to improve health care quality 15.
Analysis for multiple health care conditions takes place iteratively through the software 16, and the process just described, producing charts, graphs, and reports, using the next health care condition definition to identify the population having that condition, followed by an analysis of the claims records for that population under the defined quality care criteria for that next condition. After all the specified health care conditions have been processed in this manner, the reports for each condition are assembled into a claims-based quality report that is presented to the customer 17.
The system recognizes whether there is the need for detailed analysis. If no such need exists, no further data collection or analysis occurs. However, if a need for detailed analysis of any health care condition has been determined, then the population identified as having that condition is subjected to sampling to determine for which enrollees additional medical records information will be collected 18 (FIG. 3). With the provider's consent, the medical records are abstracted with a particular focus on events that relate to the particular health care condition under study, resulting in a completed medical records abstract form 19.
This abstracted information is then entered into the system via a personal computer to produce a medical record abstract data file 20. Charts and graphs are generated reporting statistically observed data in the population defined as having the health care condition of interest, and a report containing recommendations for actions to improve health care quality is also generated 21 (FIG. 4). If detailed analysis of medical records is specified for multiple health care conditions, then the preceding steps are repeated until charts and graphs reporting statistically observed data and a report containing recommendations for actions to improve health care quality are developed for each health care condition 22.
After all the specified health care conditions have been processed in this manner, the reports for each condition are assembled into a detail level report that is presented to the customer and the process ends 23. One major drawback of this system is that, for example, it analyzes “after-the-fact” the appropriateness of the delivered medical care for quality and cost. Further, the Leatherman et al. system is focussed on the appropriateness of medical costs for the preferred service, and does not determine patient specific services and/or patient specific future/proactive treatment prior to occurrence of medical conditions on a patient-specific basis.
U.S. Pat. No. 5,660,176 to Iliff, incorporated herein by reference, is directed to a computerized medical diagnostic and treatment advice system. Referring to FIG. 5, the components of the computerized medical diagnostic and treatment advice system are shown. A personal computer (PC) includes a plurality of components within an enclosure. A plurality of telephone lines 24 interface the public telephone network to the computer. One of telephone lines 25 is shown to be switched via network to connect with a telephone that is used by a person desiring to speak with a medical advice user 26.
The system runs on the PC with a microprocessor 27. Telephone functions use a voice processing board (VP) 28 based on a digital signal processor (DSP). A group of one to four telephone lines 29 connect to the voice processing board 28. The computer may include a plurality of VP boards based on how many phone line connections are desired for the system. Speech recognition is achieved using Voice Processing Corporation's speech recognition VPRO-4 board (voice recognition board or VR board) (which is also DSP based) 30.
The VR board 30 and the VP board 28 both connect to an industry standard architecture (ISA) bus 31. The VP board also connects to a VPRO-Adapt board 32 via an analog audio bus that is called Analog Extension Bus 33. The Adapt board further connects to a digital audio bus 34. The VR board also connects to the digital audio bus. The Adapt board performs analog to digital signal conversion to a digital pulse code modulation (PCM) format.
A video adapter board 35 interconnects to a video monitor 36. A serial communication circuit 37 interfaces a pointing device, such as a mouse 38. A parallel communication circuit may be used in place of the serial communication circuit 37 in another embodiment. A keyboard controller circuit 39 interfaces a keyboard 40. A small computer systems interface (SCSI) adapter 41 provides a SCSI bus 42 to which a 500 Mb or greater hard disk drive 43 is attached.
The hard drive stores database files such as the patient files, speech files, and binary support files. Main memory 44 connects to the microprocessor 27. An algorithm processor 45 includes a parser and supporting functions that manipulate a memory variable symbol table and a run time stack.
FIG. 6 is a block diagram illustrating a conceptual view of the database files and processes of the system from Iliff. Patient log in process 46 (FIG. 6) is used to identify a patient who has previously registered into the system. An assistant log in process 47 is used to identify an assistant who has previously registered into the system.
If the caller is the patient, a patient registration process 48 is used to register new or first-time patients. If the caller is not the patient, an assistant registration process 49 is used to register new or first-time assistants. Then, if the patient is not already registered, an assisted patient registration process 50 is used to register the patient.
The master patient and assistant enrollment database 51 is created at run-time by one of the registration processes. This database is read by the patient log in process 46 to validate a patient's identity at log in time, and by the assistant login process 47 to validate an assistant's identity at log in time. The database is essentially a master file of all registered patients and assistants indexed by their patient ID number or assistant ID number, respectively.
In Iliff, the medical advice is provided to the general public over a telephone network 24. Two new authoring languages, interactive voice response and speech recognition, are used to enable expert and general practitioner knowledge to be encoded for access by the public. Meta functions for time-density analysis of a number of factors regarding the number of medical complaints per unit of time are an integral part of the system. Thus, the system in Iliff, for example, is designed as a reactive measure to respond to caller complaints, and provides no process for ensuring and/or designing appropriate patient specific care, nor collects extensive information on a patient's use of medication(s), medical history, and/or satisfaction.
U.S. Pat. No. 5,557,514 to Seare et al., incorporated by reference, provides a mechanism for assessing medical services utilization patterns. The program achieves this object by allowing comparison processing to compare an individual treatment or a treatment group against a statistical norm or against a trend. Seare et al. also provides a mechanism for converting raw medical provider billing data 52 (FIG. 7) into an informative historical database 53. The program achieves this object by read, analyze and merge (“RAM”) processing 54 coupled with claims edit processing 55 to achieve a reliable, relevant data set. It provides a mechanism for accurately determining an episode of care. The program achieves this object by providing a sequence of steps which, when performed, yield an episode of care while filtering out irrelevant and inapplicable data. Further, Seare et al. provides a method for performing a look-up of information, that is, providing a mechanism for gaining access to different parts of the informational tables maintained in the database 53. This object is achieved by reviewing the referenced tables for specific codes representing specific diagnoses. The codes are verified for accuracy. Then tables are accessed to display selected profiles. Users are then given the opportunity to select profiles for comparison.
Searle et al. provides a method for comparing profiles. This object is achieved by comparing index codes against historical reference information stored in the parameter tables 54 (see FIG. 8). Discovered information is checked against defined statistical criteria in the parameter tables 55. The process is repeated for each index code and its profile developed in the history process as many times as necessary to complete the information gathering 56. The system creates, maintains and presents to the user a variety of report products 57. These reports are provided either on-line or in a hard copy format. The process of creating, maintaining and presenting these reports is designed to present relevant information in a complete and useful manner.
FIG. 9 from Searle et al. depicts steps performed in the method to establish a practice parameter or utilization profile for a particular diagnosis. Searle et al. includes both a system and a method for analyzing health care providers' billing patterns, enabling an assessment of medical services utilization patterns. Searle et al. determines whether a provider or multiple providers are over utilizing or underutilizing services when compared to a particular historical statistical profile. The statistical profile is a statically derived norm based on clinically-validated data which has been edited to eliminate erroneous or misleading information. The profiles may be derived from geographic provider billing data, national provider billing data, the provider billing data of a particular pay or entity (such as an insurance company) or various other real data groupings or sets. Twenty informational tables are used in the database of Searle et al. including a Procedure Description Table, ICD-9 Description Table, Index Table, Index Global Table, Index Detail Table, Window Table, Procedure Parameter Table, Category Table, Qualifying Master Table, Specialty Table, Zip/Region Table, Family Table, Speciality Statistic Table, Age/Gender Statistic Table, Region Statistic Table, Qualifying Index Table, Qualifying Group Table, Category Parameter Table, Duration Parameter Table and Family Table. Thus, Searle et al. includes similar disadvantages as the other referenced prior art references, analyzing past medical conditions, and the appropriateness therefor.
International Patent Application Publication No. WO 95/1904 by Tallman et al., incorporated herein by reference, provides a medical network management system and process system based on understanding and managing the process of care, in an integrated manner, from the onset of patient perception of possible needs. This prior art provides such a medical network management system and process which allows beneficiaries to obtain appropriate care, at the appropriate time, from an appropriate provider. It also provides such a medical network management system and process which effectively reduces utilization and costs, while increasing user satisfaction and overall quality of care. Tallman et al. provides a medical network management system and process which uses information systems to help guide patients through and manage the process of care, thereby assuring quality health care.
Nurses search the information using the criteria necessary to meet specific patient needs as identified through the assessment process. The process begins when the patient initiates a call or visits a nurse utilizing the NMS (network management system) 58 (see FIG. 10). Patient information is gathered and eligibility is confirmed by accessing data from a patient chart 59. A determination is then made whether the patient requires medical intervention, using the algorithms 60. If medical intervention is not required, home care instructions are provided 61 and a follow-up call is scheduled 62. During follow-up, a determination is made whether the problem has been resolved safely. If not, another determination is made whether medical intervention is required 63.
The medical network management system in Tallman et al. has a data processing system, including a memory (containing a patient assessment stored program and a patient database), a display, and input means. The patient assessment stored program 65 (FIG. 11) includes means for checking patient eligibility, means for selecting a branched chain logic algorithm for assessing a patient for an appropriate timing and type of medical care, and a plurality of branched chain logic algorithms. Each of the branched chain logic algorithms (see FIGS. 12, 13, and 14, for example) assess the patient for an appropriate timing and level of medical care. The data processing system is configured by the patient assessment stored program to present a series of questions on the display for checking patient eligibility to receive medical care, for selecting one of the plurality of branched chain algorithms, for guiding the patient through the selected one of the plurality of branched chain algorithms, to enter answers from the patient to the series of questions, to make a medical care timing and level of medical care recommendation in response to patient answers to the questions, and to provide the medical care timing and level of medical care recommendation on the display.
A first series of questions is presented on the display 68, 69, 70 (FIG. 12) to select one of a plurality of branched chain algorithms 71-76 which assess the patient for an appropriate timing and level of medical care. A second series of questions is presented on the display to guide the patient through the selected one of the plurality of branched chain algorithms 71-76. Answers from the patient to the second series of questions are entered in the data processing system. A medical care timing and level of medical care recommendation is made in response to patient answers to the second series of questions.
This set of information tools are used by health care professionals to assess patient conditions and assist in the selection of health care services and to help patients find appropriate care at the appropriate time. The comprehensive, automated set of proprietary assessment algorithms (for example, FIGS. 12, 13, and 14) enable a trained nurse to sort patients into different risk categories, safely and effectively by requiring a medical diagnosis. Patients can then be guided to an appropriate level and type of care for their problem(s) based on their level of risk and set of potential needs.
To understand risk sorting, consider 10,000 beneficiaries of members of a health plan. In a year, almost 1000 members of this group will become patients seeking medical care for lower back pain symptoms. Among those who receive care, there will only be a very small number of patients whose pain actually results from serious, but infrequent causes/health conditions. A series of questions, asked in the correct order, can clearly identify those patients for whom these serious causes cannot be safely eliminated. They must all receive immediate, proper medical care to actually search for any of these conditions and potentially prevent very serious consequences.
Once these patients with potential infrequent problems are identified, the remaining patients are those almost certainly experiencing some form of musculo-skeletal back pain, which is almost always self-limiting and self-correcting. None are in immediate danger of death or further injury. If symptoms persist, a higher level of medical care could then be appropriate. Through the process of asking questions and sorting patients by risk categories, safe and apparently effective treatment are claimed.
When any of the algorithms indicate that medical intervention is appropriate for a given patient, the nurse will then use the second major component of the NMS, which is described below, to assist that patient in selecting an appropriate, individual provider.
The second major component of the system consists of a proprietary relational database 66 (FIG. 11) which contains the information necessary to effectively differentiate the various providers participating in a given network and to manage the patient flow into their practices. This system component enables the nurse to help patients select an individual doctor, clinic, or hospital; i.e., an appropriate provider of the services required to meet their needs. The patient assessment component and the provider information component are linked by provider codes 67, which identify by standard procedure billing codes, what procedures the different providers perform in the normal course of their practice. The link further includes patient information, such as sex, age, zip code, health plan and other patient information useful for matching a patient to a provider.
Data describing areas of clinical expertise and the practice in general, are collected from each provider. This information is monitored and updated regularly. It can also be augmented by relevant information from other sources such as claims data and can contain items such as procedural frequency or clinical outcomes. Thus, Tallman et al. is principally based on the patient awareness of medical needs. Tallman et al. thus assumes that knowledge about a condition results in planned action to seek care. The literature is clear that, for many individuals, this is poorly correlated. In other words, Tallman et al. is a more sophisticated approach at reactively responding to medical conditions.
International Patent Application Publication No. WO 94/00817 by G. Mcilroy et al., provides a health care management data processing system that is a real-time, interactive system to manage the health care process. The system can be used by hospitals, physicians, insurance companies, HMOs, and others in the health care field to promote cost-effective health care.
The Mcilroy et al. system builds from a data base of treatment guidelines developed by medical professionals (see FIG. 15). It provides a diagnosis-based system that can be used during various steps of the clinical decision process: (1) prospectively, before treatment, when an individual presents a health concern; (2) concurrently, at any stage of existing treatment; and (3) retrospectively, after treatment as been provided. The treatment guidelines are structured to work with an interactive question and answer methodology that ensures that the most appropriate data are collected, and guides the user through the complex medical evaluation process. This is done by presenting questions in a logically structured order from the condition, leading to guideline-recommended treatment 77 (FIG. 16). The information retained by the system allows for a consistent, efficient review process. Variances between actual or proposed and guideline-recommended treatment can be used for quality assurance and audit purposes. Also, cross-specialty review is facilitated.
There is a processing unit 78 (FIG. 17) and software-implemented health condition and treatment guidelines program 79. A user inputs 80 an individual's health data into a new or existing case file (FIG. 18) in response to inquires implemented in a health condition specific guideline. Through the interactive guideline query-response process, a guideline-recommended treatment (or treatments) is obtained (FIG. 19). The user may adopt or accept the guideline-recommended treatment or input an actual or proposed treatment that is different. Discrepancies between actual/proposed and guideline-recommended treatment are identified and the user's choice is documented through interactive queries. Once a treatment is selected, the case information is added to the data base and an additional reviewer can analyze the file. The case may be re-opened, and changes may be made at any stage in the process to reflect new conditions, or new or modified treatments.
At the foundation of the system is a set of diagnosis-based guidelines that are derived from medical, professional and health care management expertise (FIG. 15). Each guideline is associated with a particular health care condition for which treatment exists. Each guideline is intended to lead a system user through a sequence of interactive data collection queries based on the specified health care condition observed in an individual patient. The data-collection queries are logically structured so that the user identifies pertinent patient characteristics and is led to an endpoint that is usually one recommended treatment (see e.g. FIG. 16). However, the endpoint may also be two or more alternative treatments, a pointer to a different guideline or a recommendation for further clinical evaluation before treatment is selected.
As implemented in the system, a guideline can be viewed as a decision tree with multiple data collection nodes (FIG. 16 and FIG. 18), most of which have conditional branching to connected nodes based on user-supplied data. The endpoints of navigation through the decision tree are usually embodied in a set of recommended treatments (e.g., FIG. 19). The path to any recommended treatment involves one or more conditional branches. Thus, each guideline implemented in the system has a definite algorithmic structure that guides the user. Mcilroy et al. therefore is more centrally focused on practice health care, and/or assisting a clinician during the diagnosis and/or treatment of the patient.
Accordingly, after review of the prior art and the needs of the health care system, we have determined that it is desirable to provide a method and/or system to facilitate the delivery of high quality services to high health resource utilizers in a population.
It has also been determined that it is desirable to provide a method and/or system that eliminates and/or manages redundant, repetitive, medical and/or pharmaceutical related information for better utilization of resources in conducting same.
It has also been determined that it is desirable to provide a method and/or system that identifies dynamically or in real-time candidates for other health care management programs.
We have also determined that it is desirable to provide a method and/or system that improves the clinical information for use in patient specific drug utilization review, care management, and prior authorization programs, improving the overall quality of care provided.
We have also determined that it is desirable to provide a method and/or system that provides a central portfolio of patient specific data for assessment of the effects of health management programs through the selection and/or collection of extensive information on patient specific medical claims, drug claims, an interactive interview and/or demographic information.
We have also determined that it is desirable to have a method and/or system that incorporates lifestyle and/or psychosocial indicators with clinical data to more effectively tailor health care messages and to change behavior.
We have also determined that it is desirable to provide a computer implemented and/or assisted process for creating and administering a set of core patient specific survey questions and combining the patient specific information obtained from the single survey or multiple surveys with patient specific diagnostic data to accurately predict risk of an event or progression advancement in medical conditions which are likely to result in high utilization of health resources in an efficient and timely manner for a pre-selected and/or targeted sub-patient population from one or more larger patient populations, health care organization groups and the like.